knitr::opts_chunk$set(echo = FALSE, message = FALSE)
library(Seurat)
library(ggplot2)
library(data.table)
library(MAST)
library(SingleR)
library(dplyr)
library(tidyr)
library(limma)
library(scRNAseq)

Loading Data

Basic Plots

## Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
## Please use `as_label()` or `as_name()` instead.
## This warning is displayed once per session.

Cluster Naming

SingleR

Reference Datasets * ImmGen Data: normalized expression values of 830 microarray samples of pure mouse immune cells, generated by the Immunologic Genome Project * Mouse RNA Seq Data: normalized epxression values of 358 bulk RNA-seq samples of sorted cell populations found on GEO

Using all cells

Using on Wildtype

Marker Gene Expression

Cell type specific marker gene expression. Genes were added to the list in two different ways: canonical markers that are well known in the field, and genes that distinguished clusters and were found to play a key role in specific cells.

Literature for Markers: Previously used

Ighd: immunoglobulin heavy constant delta. Seems to clearly be expressed by B-cells, but still working on a good reference.

Gata2: From Krause paper: a transcription factor required for both lineages but bind in different combinations ref

Cd68: a human macrophage marker ref. A more general ref

Vcam1: found papers using Vcam1+ monocytes, but haven’t found a great reference.

Alas2: an erythroid-specfiic 5-aminolevulinate synthase gene ref

Gata3: plays a role in the regulation of T-cells ref

Vwf and Itga2b: Markers for megakaryocytes

Mcpt8 and Prss34: mast cell proteases

Progenitor Markers

Alt Text

HSPCs

Reading through this paper it states that in mice all long-term HSCs are Hoxb5+

Other markers for HSPCs: Kit, Flt3 (Negative), Ly6a, Cd34, Slamf1.

Looking just at wildtype

Looking at all cells

CMPs

We saw in the SingleR results that CMPs had the highest correlation with stem cells. From the above figure we can see that CMPs show a distinct pattern of cell surface markers: Kit+Sca1-/lowCd34+FcgRlow

Looking at just wildtype

  • Kit+: we see all CMPs positive for Kit expression
  • Sca1-/low: we do see some expression of Ly6a but where we do it is low
  • Cd34+: we see high average expression, with some null values
  • FcgRlow: we see relatively low to no expression of Fcgr2b, this gene is part of the FcgR surface marker but not sure how well it correlates

Conclusion: the identification of CMPs seems pretty spot on

Looking at all cells

Quantification of Cells

By Experiment

By State

Differential Expression

MEP/Mast Cluster

Comparing Mpl to Migr1

## [1] "Top 6 Up-Regulated DE Genes"
##                p_val avg_logFC pct.1 pct.2    p_val_adj
## Slpi    2.724286e-23  2.864115 0.964 0.739 4.933955e-19
## Akr1c18 2.491254e-15  2.638171 0.835 0.043 4.511911e-11
## Ccl4    6.207123e-12  2.556199 0.911 0.391 1.124172e-07
## Furin   9.882977e-31  1.838023 0.989 0.783 1.789906e-26
## Cfp     5.379440e-16  1.594472 0.940 0.348 9.742703e-12
## Ccl6    3.882870e-21  1.450810 0.978 0.739 7.032266e-17
## [1] "Top 6 Down-Regulated DE Genes"
##               p_val avg_logFC pct.1 pct.2    p_val_adj
## Hmgb2  6.395122e-11 -1.211850 0.927 1.000 1.158220e-06
## Lmo4   1.686466e-26 -1.281644 0.377 0.739 3.054359e-22
## Nedd4  4.065083e-27 -1.368005 0.788 1.000 7.362271e-23
## Csrp3  3.630654e-32 -1.604731 0.287 0.913 6.575477e-28
## Mpo    1.815986e-17 -1.634382 0.314 0.478 3.288933e-13
## S100a9 9.667419e-07 -1.976548 0.998 0.957 1.750866e-02

Subclustering of MEP/Mast Cluster

## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session

## 
##   0   1   2   3   4   5   6 
## 190 166  85  74  36  27  14
##    
##     Migr1 Mpl Wildtype
##   0     8 180        2
##   1     0 166        0
##   2     2  83        0
##   3    10  48       16
##   4     0  36        0
##   5     0  27        0
##   6     3  11        0

Mast Markers in Subclustering

Seems like wide expression in all the clusters, with the exception of relatively low expression of Prss34 in subcluster 5 (all Mpl)

MEP Markers

From the cell surface marker diagram shown earlier MEPs would follow this trend Kit+Ly6a-Cd34-Fcgr2b-

No specific subclustering showing that pattern. Thought once again we see subcluster 5 has the greatest Kit expression.

Krause Paper Genes

MEP Genes

MEP Genes 2

CMP Genes

ERP Genes

## Warning in FetchData(object = object, vars = features, cells = cells): The
## following requested variables were not found: Ahspp

MKP Genes

### MEP/ERP

MEP/MKP

## Warning in FetchData(object = object, vars = features, cells = cells): The
## following requested variables were not found: C3orf58

CMP/MEP

MEP-MKP-ERP

## Warning in FetchData(object = object, vars = features, cells = cells): The
## following requested variables were not found: C6orf25

Answering Potential Questions from Joint Lab Meeting